VI. Earth Life Emergence: Development of Body, Brain, Selves and Societies

2. Systems Neuroscience: Multiplex Networks and Critical Function

When this section to document 21st century advances about every aspect of our brain anatomy and cognitive performance was first posted in 2004, the fields of scale-free networks, self-organizing complexities, neuroimaging techniques, computational capacities, genetic influences and consciousness studies were at an early stage, just getting going. Circa 2108, neuroscientist researchers have now achieved a good grasp of how we think, learn, remember, speak, experience, feel, respond, cooperate and be creative. A brain’s development and active cognizance is seen to arise from a dynamic hierarchy of multiplex neural networks, modules, communities, hubs and linkages, poised in an optimum critical state. Around 2010 this sensory system was dubbed a “connectome,” akin to other -omic phases. Whole brain studies and integrations are moving toward global workspace, integrated information, and predictive expectation working model explanations.

To personalize the endeavor, among many contributors we note Giorgio Ascoli, Danielle Bassett, Gyorgy Buzsaki, Antonio Damasio, Steven Grossberg, Karl Friston, Michael Gazzaniga, Suzana Herculano-Houzel, Christoph Koch, Sebastian Seung, Hava Siegelmann, Olaf Sporns, and Giulio Tononi, everyone herein, and other sections throughout. Indeed, to fulfill this promise, national and international collaborative brain projects, akin to the human genome, are underway in the United States, Europe, Japan, and elsewhere. In regard, this neural network intricacy and acumen has become an iconic microcosm to an extent that, as Earthificial Intelligence notes, its deep learning abilities are used to analyze and discover from quantum to cultural realms. By this view, an AnthropoCosmo sapiensphere could even be seen to retrospectively quantify, and palliate, the myriad evolved personal organic brains from which it altogether fitfully arose.

Agnati, Luigi, et al.
On the Nested Hierarchical Organization of CNS.
Erdi, Peter, et al, eds.
Computational Neuroscience: Cortical Dynamics.
Berlin: Springer, 2004.
A collaboration of ten authors from biochemical, neurological and medical sciences find the Central Nervous System to possess scalar realms from macromolecules to molecular networks, systems of these, local higher circuits, cellular networks, and their somatic systems. The same properties and dynamics occur at each stage which suggests a fractal self-similarity as its “animating principle.” In this view, our neurological soma appears as an iconic microcosm of how a genesis nature organizes itself and proceeds to knowing intelligence.

If we accept the view of the CNS as a nested hierarchical complex system, it is possible to search for schemes of functional organization at the various miniaturization levels. It is suggested that basically the same schemes for communication and elaboration of the information are in operation at the various miniaturization levels. This functional organization suggests a sort of “fractal structure” of the CNS. As a matter of fact, according to fractal geometry, fractal objects have the property that as we magnify them, more details appear but the shape of any magnified detail is basically the same as the shape of the original object. It is, therefore, suggested to introduce the term “fractal logic” to describe networks of networks where at the various levels of nested organization the same principles (logic) to perform operations are used. (29-30)

Agnati, Luigi, et al.
One Century of Progress in Neuroscience Founded on Golgi and Cajal’s Outstanding Experimental and Theoretical Contributions.Brain Research Reviews.
55/1,
2007.
A retrospective on the original Nobel prize insights of Santiago Ramon y Cajal and Camillo Golgi and an illustrated survey of the state of brain science today. A global theory of both form and function is at last possible via a nested hierarchy of fluid networks from the neurons that Ramon y Cajal first identified to the holistic cerebrum that Golgi advocated. And it ought to be noted that the worldwide computer web is taking on the same architecture and cogitation.

Allen, Micah and Karl Friston.
From Cognitivism to Autopoiesis: Toward a Computational Framework for the Embodied Mind.Synthese.
195/6,
2018.
University College London neuroscientists embellish their theories about our constant anticipatory perceptions by noting affinities with enactive embodiment and constructionist, self-making approaches (each malleable terms). These integrations allow prior representations, along with on-going experiential influences, to be accommodated. We surely live each day looking forward, but with reference to ingrained expectations. See also Friston’s publication page at the Wellcome Trust Centre for Neuroimaging for more contributions. On the arXiv eprint site can be found, for example, A Computational Hierarchy in the Human Cortex (1709.02323) and How Robust are Deep Neural Networks (1804.11313).

Predictive processing (PP) approaches to the mind are increasingly popular in the cognitive sciences. In particular, the question of how to position predictive processing with respect to enactive and embodied cognition has become a topic of intense debate. Here, we present a basic review of neuroscientific, cognitive, and philosophical approaches to PP, to illustrate how these range from solidly cognitivist applications—with a firm commitment to modular, internalistic mental representation—to more moderate views emphasizing the importance of ‘body-representations’, and finally to those which fit comfortably with radically enactive, embodied, and dynamic theories of mind. We go on to illustrate how the Free Energy Principle (FEP) attempts to dissolve tension between internalist and externalist accounts of cognition, by providing a formal synthetic account of how internal ‘representations’ arise from autopoietic self-organization. The FEP thus furnishes empirically productive process theories (e.g., predictive processing) by which to guide discovery through the formal modelling of the embodied mind. (Abstract edits)

The free energy principle tries to explain how (biological) systems maintain their order (non-equilibrium steady-state) by restricting themselves to a limited number of states. It says that biological systems minimise a free energy functional of their internal states, which entail beliefs about hidden states in their environment. The implicit minimisation of variational free energy is formally related to variational Bayesian methods and was originally introduced by Karl Friston as an explanation for embodied perception in neuroscience, where it is also known as active inference. (Wikipedia)

Almeida e Costa, Fernando and Luis Mateus Rocha.
Introduction to the Special Issue: Embodied and Situated Cognition.Artificial Life.
11/1-2,
2005.
Whose papers scope out a more realistic, animate context for Alife studies. An example is Smith and Gasser’s paper in the previous section.

The embodied cognition approach thus moved the modeling of intelligent systems from the study of intricate knowledge-based, representation-rich control systems to the study of the dynamics of networks of agent and environment components (self-organization). (6) In this alternative view, cognition is no longer modeled as the creation of agent-independent representations of the world, but as the embodied, evolving interaction of a self-organizing system with its environment. (6)

Altamura, Mario, et al.
Toward Scale-Free Like Behavior under Increasing Cognitive Load.Complexity.
Online June,
2012.
University of Foggia, Italy, University of Tromso, Norway, Institute of Crystallography, CNR, Rome, and Deutsches Elektronen-Synchrotron DESY, Hamburg researchers find that a responsive, thoughtful human brain, as an archetypla nonlinear dynamical system, can be found to progressively move through phase transitions to emergent states of fractal criticality.

To understand how cognition and response selection processes might emerge from dynamic brain systems, we analyzed reaction times during the performance of both a working memory task and a choice reaction time task at different levels of “cognitive load.” Our findings suggest a continuous transition—tuned by load—from random behavior toward scale-free like behavior as an expanding connectivity process in a network poised near a critical point. (Abstract)

Anderson, James and Edward Rosenfeld, eds.
Talking Nets.
Cambridge: MIT Press,
1998.
A series of interviews with the originators of neural network theory such as David Rumelhart, Teuvo Kokonen, and Stephen Grossberg.

I claim that, in order to self-organize intelligent adaptive processes in real time, the brain needs nonlinear feedback processes that describe dynamical interactions among huge numbers of units acting on multiple spatial and temporal scales. (Grossberg 195)

As we have seen, the brain is considered a prototype of hierarchical structures: Neural systems can be studied at one or more levels, such as the molecular, membrane, cellular, synaptic, network, and system levels….Both ontogenetic development and phylogenetic evolution are dynamic processes to be identified with self-organization phenomena. (4)

Ariswalla, Xerxes and Paul Verschure.
The Global Dynamical Complexity of the Human Brain.Applied Network Science.
Online December,
2016.
(arXiv:1612.07106). University of Pompeu Fabra, Barcelona, cognitive informatics researchers at once recognize the growing value of the integrated information theory (Tononi, et al), which is in need of a further finesse (Oizumi). As the Abstract cites, this is achieved by insights into and avail of active network topologies. Each author’s publications page has more papers, for example see Connectomics to Semantomics (Arsiwalla herein).

How much information do large brain networks integrate as a whole over the sum of their parts? Can the dynamical complexity of such networks be globally quantified in an information-theoretic way and be meaningfully coupled to brain function? Recently, measures of dynamical complexity such as integrated information have been proposed. However, problems related to the normalization and Bell number of partitions associated to these measures make these approaches computationally infeasible for large-scale brain networks. Our goal in this work is to address this problem. Our formulation of network integrated information is based on the Kullback-Leibler divergence between the multivariate distribution on the set of network states versus the corresponding factorized distribution over its parts. We find that implementing the maximum information partition optimizes computations. These methods are well-suited for large networks with linear stochastic dynamics. We compute the integrated information for both, the system's attractor states, as well as non-stationary dynamical states of the network. We then apply this formalism to brain networks to compute the integrated information for the human brain's connectome. (Abstract)

Arshavsky, Yuri.
Role of Individual Neurons and Neural Networks in Cognitive Functioning of the Brain.Brain and Cognition.
46/414,
2001.
An observation that discrete neurons are not wholly controlled by network dynamics but operate in a distinct “cell-autonomous” manner, a cerebral example of the generic particle/wave, agent/relation complementarity.

Ascoli, Giorgio.
Trees of the Brain, Roots of the Mind.
Cambridge: MIT Press,
2015.
At the frontiers of natural philosophy, a George Mason University, Center for Neural Informatics, Structure and Plasticity neuroscientist surveys the 2010s era of sophisticated 3D cerebral imaging research. By virtue of these capabilities and their findings, a forest analogy is evoked for branching networks of local, arboreal neurons, synapses and axons whose intricate nestedness forms a whole brain anatomy. This vista of myriad nets within Nets leads to novel insights, as the quotes sample. Our neural connectome thus learns, thinks, and responds in a mindful way by necessarily comparing new experience with prior representations contained in the dynamic network tree topology. In a conclusion, this “brainforest” environment is seen as an apt microcosm for a similar encompassing network nature.

We have so far described how the third principle of the brain-mind relationship explains why appropriate knowledge of relevant background information gates the acquisition of new information by one-trial learning. The relationship between axonal-dendritic overlaps (corresponding to the potential to learn) and existing connectivity (representing knowledge) provides a direct neural mechanism for the familiar observation that experts can grasp new concepts in their discipline much faster than novices. Everyone finds it easier to learn new facts in their domains of expertise than in a completely novel field. (108)

Even our incomplete comprehension of neuronal structure and function has allowed us nonetheless to propose three core principles for linking the nervous system with the mind. We hypothesized in the first principle an identification of mental states and patterns of electric activity in the brain. A consequence of this equivalence is that knowledge, the ability to instantiate a given mental stare, is encoded in the connectivity of the network because electric activity in nervous systems flows through connections among neurons. This consideration led to formulation of a structural mechanism for what it means to acquire new knowledge, that is, to learn something. Specifically, in the second principle we equated learning to a change in network connectivity through the creation and removal of synapses. (181)

The third principle, which merely builds on the logic of the first two, is nevertheless the most radical and novel proposition of this book: that the spatial proximity of axons and dendrites, enabling synapse formation and elimination, corresponds to the capability for learning. Such a correspondence has far-reaching implications because it directly ties the branching structure of neurons to the fine line separating nurture from nature. Without the constraint of physical proximity between axons and dendrites, we could be able to learn anything from experience. With this rule in place, in constrast, each of us learns only those aspects of experience that are somehow compatible with our existing knowledge. (182)

Based on the principles exposed in this book, we can attempt to revisit the question of Reality. In their bare computational essence, brains can be viewed as gigantic networks whose sets of connections represent associations of observables learned through experience. We can thus offer a radical perspective of Reality. Reality constitutes an enormous interconnected web of co-occurring events. Each pair of events can be quantitatively expressed in the context of the entire web by the conditional probability representing the information content of their co-occurrence. Every human being (as well as, of course, all other animals and inanimate objects) is immersed in this universal web. From within, each person at any given moment witnesses a small fraction of co-occurring events based on his or her location, time, state of attention, and so forth. Brains evolved as networks (of neurons) themselves in order to represent most effectively the surrounding reality, thereby gaining predictive power and endowing their carriers with survival fitness. Such integration provides a natural conceptual framework to characterize the interactions among the world, the brain, and the mind. (211)

Ascoli, Giorgio and Diek Wheeler.
In Search of a Periodic Table of the Neurons: Axonal-Dendritic Circuitry as the Organizing Principle.BioEssays.
Online August,
2016.
George Mason University computational neuroanatomists lay out a pioneer paper that tries to conceive an intrinsic architectural scale akin to the chemical elements. A longer subtitle is Patterns of axons and dendrites within distinct anatomical parcels provide the blueprint for circuit-based neuronal classification. See also prior work Towards the Automatic Classification of Neurons in Trends in Neuroscience (36/5, 2015) and Name-Calling in the Hippocampus: coming to Terms with Neuron Types and Properties in Brain Informatics (Online June 2016).

No one knows yet how to organize, in a simple yet predictive form, the knowledge concerning the anatomical, biophysical, and molecular properties of neurons that are accumulating in thousands of publications every year. The situation is not dissimilar to the state of Chemistry prior to Mendeleev's tabulation of the elements. We propose that the patterns of presence or absence of axons and dendrites within known anatomical parcels may serve as the key principle to define neuron types. Just as the positions of the elements in the periodic table indicate their potential to combine into molecules, axonal and dendritic distributions provide the blueprint for network connectivity. Furthermore, among the features commonly employed to describe neurons, morphology is considerably robust to experimental conditions. At the same time, this core classification scheme is suitable for aggregating biochemical, physiological, and synaptic information. (Abstract)

Ashourvan, Arian, et al.
The Energy Landscape Underpinning Module Dynamics in the Human Brain Connectome. arXiv:1609.01015.
From this active University of Pennsylvania neuroscientist team including Danielle Bassett and Marcelo Mattar an entry that considers integral rootings of cerebral cognition into basic physical principles. A companion paper by the group is Brain Network Architecture: Implications for Human Learning at 1609.01790. Along with similar work by Edward Bullmore and Aharon Azulay (search each), a mindful, intelligent natural cosmos which becomes sentient and knowledgeable in its phenomenal human phase is increasingly revealed.

Human brain dynamics can be profitably viewed through the lens of statistical mechanics, where neurophysiological activity evolves around and between local attractors representing preferred mental states. Many physically-inspired models of these dynamics define the state of the brain based on instantaneous measurements of regional activity. Yet, recent work in network neuroscience has provided initial evidence that the brain might also be well-characterized by time-varying states composed of locally coherent activity or functional modules. Here we study this network-based notion of brain state to understand how functional modules dynamically interact with one another to perform cognitive functions. We estimate the functional relationships between regions of interest (ROIs) by fitting a pair-wise maximum entropy model to each ROI's pattern of allegiance to functional modules. These results collectively offer a view of the brain as a dynamical system that transitions between basins of attraction characterized by coherent activity in small groups of brain regions, and that the strength of these attractors depends on the cognitive computations being performed. (Abstract excerpts)